Efficiency - smaller fine-tuned models vs. large general models
The Model Customization Spectrum
flowchart LR
A[Prompt Engineering] -->|Increasing complexity/cost| B[Retrieval Augmented Generation]
B --> C[Fine-Tuning]
C --> D[Continued Pre-training]
E[Pre-training from Scratch]
D --> E
style A fill:#d4f1f9
style B fill:#c2e5d3
style C fill:#ffe0b2
style D fill:#ffccbc
style E fill:#ffaba0
Cost-Benefit Analysis
Approach
Cost
Time
Data Required
Performance Improvement
Prompt Engineering
$
Hours-Days
Low
Low-Medium
RAG
\[ | Days | Medium | Medium |
| Fine-Tuning | \]$
Days-Weeks
Medium-High
Medium-High
Continued Pre-training
$\[$ | Weeks-Months | High | High |
| Pre-training | \]$$$
Months
Massive
Complete control
Traditional Fine-Tuning vs. PEFT
Full Fine-Tuning
Updates all model parameters
Requires large amounts of GPU memory
More expensive computationally
More prone to catastrophic forgetting
Requires more data to avoid overfitting
Parameter-Efficient Fine-Tuning
Updates small subset of parameters
Much lower memory requirements
Computationally efficient
Better preserves general capabilities
Works well with limited data
Parameter-Efficient Fine-Tuning (PEFT)
Adapter methods: Add small trainable modules to frozen model
Data augmentation: Techniques to artificially expand dataset
Synthetic data generation: Using existing LLMs to create training data
Instruction Tuning
INSTRUCTION: Write a poem about artificial intelligence in the style of Shakespeare.
RESPONSE: Hark! What light through silicon valley breaks?
It is the east, and Artificial Intelligence is the sun.
Arise, fair algorithms, and kill the envious human,
Who is already sick and pale with grief,
That thou, AI, art far more advanced than they.
...
Fine-Tuning Architectures
Encoder-Decoder Models
BART, T5, Flan-T5
Well-suited for:
Summarization
Translation
Question answering
Structured generation
Decoder-Only Models
GPT family, LLaMA, Mistral
Well-suited for:
Open-ended generation
Dialogue
Creative writing
Code generation
Tools and Frameworks for Fine-Tuning
Hugging Face Transformers & PEFT: Most common academic/research approach